Adaptive Sliding Mode Neural Network Control for Nonlinear Systems by Yang Li
Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering.-
Fundamental Design and Automation Technologies in Offshore Robotics
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Dynamics and Advanced Motion Control of Off-Road UGVs
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Vibration Control and Actuation of Large-Scale Systems
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Integer Optimization and its Computation in Emergency Management
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Consensus Tracking of Multi-agent Systems with Switching Topologies
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New Trends in Observer-based Control
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Advanced Seat Suspension Control System Design for Heavy Duty Vehicles
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Modeling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators
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Fractional-Order Design
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Fractional-Order Modeling of Dynamic Systems with Applications in Optimization, Signal Processing, and Control
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Non-monotonic Approach to Robust H8 Control of Multi-model Systems
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Recent Advances in Chaotic Systems and Synchronization
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Adaptive Identification and Control of Uncertain Systems with Non-smooth Dynamics
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Cooperative Control of Multi-Agent Systems
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Cloud Control Systems
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Fractional Order Systems
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Modeling, Optimization, and Control of Zinc Hydrometallurgical Purification Process
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Control Strategy for Time-Delay Systems
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Analysis and Synthesis of Singular Systems
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Mechanisms
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Adaptive Backstepping Consensus Control for Nonlinear Multi-Agent Systems
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Small Sample Modelling Based on Deep and Broad Forest Regression
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Composite Disturbance Rejection Control (CDRC) for Complex Dynamic Systems
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Control of Underactuated Mechanical Systems
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Internet of Things (IoT) - An Engineering Approach
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Autonomous Mobile Robots
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System Assurances
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Fast Satellite Attitude Maneuver and Control
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State Space Systems With Time-Delays Analysis, Identification, and Applications
The book is well organized and presents the most important adaptive sliding mode neural network control method for nonlinear systemsSuitable for senior undergraduate and graduate students as well as practical engineers, scientists and researchers interested in adaptive sliding mode neural network control for nonlinear system. --zbMATH
Yang Li. Lecturer at the Institute of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China. She obtained her PhD from Yanshan University, China in 2014. She was a visiting student of the University of the West of England in 2012. Her main research interests are in the areas of sliding mode control, neural network control, time delay system and control applications. Jianhua Zhang. Associate professor in Hebei University of Science and Technology, Shijiazhuang, China. He obtained his BS degree from Jilin Normal University, China in 2003, his MS degree from Yanshan University, China in 2006 and his PhD from Yanshan University, China in 2011. His main research interests are in the areas of non-linear control systems, control systems design over network and intelligent control. Mrs Wu obtained her BA and MA degrees from Hebei University of Science and Technology, China in 2010 and the University of Sheffield, England in 2011, respectively. Her main research interests are in the areas of Corpus Linguistics, Translation, and Language teaching.
| SKU | Unavailable |
| ISBN 13 | 9780128153727 |
| ISBN 10 | 0128153725 |
| Title | Adaptive Sliding Mode Neural Network Control for Nonlinear Systems |
| Author | Yang Li |
| Series | Emerging Methodologies And Applications In Modelling Identification And Control |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Elsevier Science Publishing Co Inc |
| Year published | 2018-11-21 |
| Number of pages | 186 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |